The folder MVWproject of the demonstrations can be downloaded.
The content is as follows:

…

outputconcurrency

outputpipelineAOSD10

outputpipelineIO

outputsimpleexample

outputsimpleexample2

for each output folder, (i.e., location where files are generated):

* to trace the analysis of the workflow, feature models are serialized in corresponding service subfolders (e.g. FService1) and in the MERGED subfolder
* end-result feature models are serialized in subfolder workflow

In grid-based scientific applications, building a workflow essentially involves composing parameterized services describing families of services and then configuring the resulting workflow product line. In domains (e.g., medical imaging) in which many different kinds of highly parameterized services exist, there is a strong need to manage variabilities so that scientists can more easily configure and compose services with consistency guarantees.
In this paper, we propose an approach in which variable points in services are described with several separate feature models, so that families of workflow can be defined as compositions of feature models.
A compositional technique then allows reasoning about the compatibility between connected services to ensure consistency of an entire workflow, while supporting automatic propagation of variability choices when configuring services.

Feature modeling is a widely used technique in Software Product Line development. Feature models allow stakeholders to describe domain concepts in terms of commonalities and differences within a family of software systems. Developing a complex monolithic feature model can require significant effort and restrict the reusability of a set of features already modeled. We advocate using modeling techniques that support separating and composing concerns to better manage the complexity of developing large feature models. In this paper, we propose a set of composition operators dedicated to feature models. These composition operators enable the development of large feature models by composing smaller feature models which address well-defined concerns. The operators are notably distinguished by their documented capabilities to preserve some significant properties.

SOA is now the reference architecture for medical imaging processing on the grid. Imaging services must be composed in workflows to implement the processing chains, but the need to handle end-to-end qualities of service hampered both the provision of services and their composition.
This paper analyses the variability of functional and non functional aspects of this domain and proposes a first architecture in which services are organized within a product line architecture and metamodels help in structuring necessary information.

In medical image analysis, there exist multifold applications to grids and service-oriented architectures are more and more used to implement such imaging applications.
In this context, workflow and service architects have to face an important variability problem related both to the functional description of services, and to the numerous quality of service (QoS) dimensions that are to be considered.
In this paper, we analyze such variability issues and establish the requirements of a service product line, which objective is to facilitate variability handling in the image processing chain.